The integrity and trust layer for AI training data.

Judge every source. Check every sample. Sign the evidence.

Training data comes from sources that change quietly. A vendor feed degrades. A scraping pipeline starts collecting junk. A labeling partner drifts. At the sharp end, someone deliberately slips poisoned samples into the stream. The model trains anyway.

When it misbehaves months later, most teams cannot say which data caused it, and cannot prove what the model learned from. Data quality checks run once, as scripts before training. Nothing stands between your sources and your training set, sample by sample, day after day. That is the gap Agentiks closes.

A sample can look fine on its own. The real questions are who sent it, what they sent before, and what else arrived alongside it.

Agentiks judges the source's whole history, not each sample in isolation. Trust is earned per source, never assumed per dataset.

• Source trust scoring. Every data source carries a reputation that rises and falls with its track record. A source that starts sending bad data loses standing before it can do real damage.
• Per-sample verification. Every incoming sample is checked at intake, in layers: provenance capture (where it came from, how it entered the pipeline), statistical screening, and embedding-space analysis (comparing samples as numeric vectors that capture their content). Each sample gets a pass, quarantine, or reject verdict.
• Tamper-evident audit. Every verdict lands in a cryptographically signed, append-only ledger and is bundled into integrity certificates you can hand to a reviewer, a customer, or a regulator.
• Self-hosted. Runs inside your own Kubernetes cluster. Your data never leaves your environment. Nothing phones home.

• Label flipping: clean samples arriving with wrong labels
• Sybil campaigns: one actor posing as many independent sources
• Trust grooming: a source that builds a good record, then turns
• Backdoored samples: hidden trigger patterns stamped into otherwise normal data
• Silent drift: a once-good source that quietly degrades

Integrity certificates give machine-learning, fraud, and model-risk teams audit-ready evidence for data-governance obligations such as Article 10 of the EU AI Act.

_{Prove what your model learned from.}